Hoping to address these challenges, a large portion of the style transfer community has turned to developing algorithms that operate in the feature space of a neural network, rather than using the raw pixels of patches or hand-crafted features extracted from same. Not only is it easier to blend features in the hidden state of a neural network rather than in pixel space [1]}, but the higher quality features facilitate better matching between regions of the content and style. The most commonly used neural representations in style transfer are derived from networks pretrained for image classification on photographs. In order to successfully classify the semantic contents of images, these networks learn to produce a hierarchy of features ranging from edges in the early layers, to semantic part detectors in later layers (for example dog faces or human legs).
